KAUST researchers led by Dr. Muhammad Hussain have developed a flexible, transparent silicon-on-polymer based FinFET inspired by the folded architecture of the human brain's cortex. The team created a 3D FinFET on a flexible platform without compromising integration density or performance. They aim to demonstrate a fully flexible silicon-based computer by the end of the year. Why it matters: This research could lead to the development of ultra-mobile, foldable computers and integrated circuits, advancing the field of flexible electronics in the region.
A KAUST team led by Prof. Hussain published a paper in ACS Nano detailing their use of industry-compatible processes to create a flexible transistor with a bending radius of 0.5 mm. The transistor is constructed from a monocrystalline silicon-based substrate and uses a process that does not degrade device performance. The team's approach uses a network of trenches/holes and a back-etch process to create flexible electronics without compromising cost, yield, performance, and efficiency. Why it matters: This research paves the way for high-performance, portable electronics using silicon, a material already widely used in the electronics industry.
A professor from EPFL (Lausanne) gave a talk at MBZUAI on computing in the post-Moore era, highlighting the slowing of Moore's Law due to physical limits in transistor miniaturization. He discussed research challenges and opportunities for future computing technologies. He presented examples of post-Moore technologies he helped develop in the datacenter space. Why it matters: As Moore's Law slows, research into alternative computing paradigms becomes critical for the continued advancement of AI and digital services in the UAE and globally.
KAUST researchers collaborated with TSMC to review the potential of 2D materials in overcoming silicon limitations for microchips. They find that while 2D materials show promise, performance degrades when using scalable fabrication techniques like chemical vapor deposition. 2D materials have been integrated into some commercial products like sensors, but high-integration-density circuits are still a challenge. Why it matters: This research highlights the ongoing efforts and remaining hurdles in utilizing novel materials to advance semiconductor technology in line with industry roadmaps.
A KAUST team led by Hossein Fariborzi won second place in the MEMS Design Contest for their "MEMS Resonator for Oscillator, Tunable Filter and Re-Programmable Logic Applications." The device is runtime-reprogrammable, allowing the function of each device in the circuit to be changed during operation. The KAUST team demonstrated that two MEMS resonators could replace over 20 transistors in applications like digital adders, reducing digital circuit complexity. Why it matters: This innovation could significantly reduce power consumption, chip area, and manufacturing costs in microprocessors, advancing the development of energy-efficient microcomputers in the region.
This article discusses the application of AI in semiconductor chip design and manufacturing, with a focus on examples such as IR-drop estimation and lithography processes. It mentions Youngsoo Shin, a KAIST professor and founder of Baum, who is an expert in this area. The article also briefly mentions panel discussion hosted by MBZUAI. Why it matters: AI-driven chip design and manufacturing could accelerate semiconductor innovation in the GCC region and beyond.
KAUST researchers have integrated a hexagonal boron nitride sheet into CMOS microchips, creating a hybrid 2D-CMOS microchip. This integration leverages the electrical and thermal properties of 2D materials, resulting in circuits that are smaller, more energy-efficient, and have longer lifespans. The KAUST Imaging and Characterization Core Lab contributed to the observations in this study, which involved researchers from six countries. Why it matters: This achievement represents a significant advancement in microchip miniaturization and performance, potentially impacting various electronic applications.
This paper analyzes the impact of device uncertainties on deep neural networks (DNNs) in emerging device-based Computing-in-memory (CiM) systems. The authors propose UAE, an uncertainty-aware Neural Architecture Search scheme, to identify DNN models robust to these uncertainties. The goal is to mitigate accuracy drops when deploying trained models on real-world platforms.